Telecom Traffic Engineering & Teletraffic Calculators

Carrier-grade dimensioning models for telephony trunks, contact center queuing, SIP concurrency, and radio voice capacity. Accurately solve Erlang B blocking probabilities, Erlang C wait times, Engset finite subscriber pools, and packet voice bandwidth constraints.

9 Production Calculators Zero External Dependencies Numerically Stable Algorithms ITU-T Q.543 Compliant Classical & Packet Queuing

Instant Erlang B Trunk Capacity Quick-Lookup

Real-Time Lost-Calls-Cleared (M/M/m/m) Blocking Probability Evaluator
Recursive Formula: B(k, A) = A·B / (k + A·B)
Erlangs
trunks
Calculates peak busy hour blocking probability without factorial overflow. Adjust inputs above for instant recalculation.
Blocking Probability (Pb / GoS) 2.11% (P.0211)
Grade of Service Commercial (P ≤ 2.5%)
Carried Load: 14.68 E
Lost Traffic: 0.32 E
Occupancy (ρ): 66.7%
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Teletraffic Engineering & Queuing Directory

Showing 9 of 9 Calculators
Trunking & PBX M/M/m/m

Erlang B & Erlang C Voice Trunk Capacity Calculator

Dimension voice trunks, PBX lines, and PSTN interconnects under the classical M/M/m/m lost-calls-cleared model with zero queuing. Calculate call blocking probability (Grade of Service) or solve for required channels.

Inputs: Offered Traffic (E), Available Trunks (m), Target GoS (P.01)
Outputs: Blocking Prob Pb, Carried Load, Trunk Occupancy ρ
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Contact Center & Queuing M/M/m

Erlang C Call Center Staffing & Queuing Calculator

Model call center queue delay, Average Speed of Answer (ASA), agent staffing counts, and 80/20 SLA probabilities using M/M/m queuing with infinite queue capacity and exponential service holding times.

Inputs: Call Volume (calls/hr), AHT (sec), Agent Count (m), Target SLA Time (t)
Outputs: Probability of Delay Pc, ASA, Service Level %, Queue Length Lq
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Trunking & PBX M/M/m/m (Retrial)

Extended Erlang B Calculator (Caller Retry & Recall Factor)

Account for subscriber retrial psychology where blocked callers immediately redial, inflating true carried load on constrained trunks. Iteratively converges offered load to model real-world overload conditions.

Inputs: Raw Offered Traffic (A), Trunk Count (m), Retry Probability (R: 0–100%)
Outputs: Effective Inflated Traffic A', Final Blocking Rate Pb, Retrial Load
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Trunking & PBX M/M/m/m/S

Engset Model Calculator (Finite Subscriber Populations)

Calculate blocking probability when the source population S is small relative to servers m (S / m < 10), where infinite Poisson assumptions fail. Accurately evaluates on-hook source rates and state-dependent arrivals.

Inputs: Number of Sources (S), Number of Channels (m), Traffic per Idle Source (α)
Outputs: Call Congestion Pcall, Time Congestion Ptime, Carried Traffic
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Traffic Conversion A = λ·h / 3600

Busy Hour Traffic & Erlang Converter (BHCA / CCS / Erlangs)

Convert bidirectionally between Busy Hour Call Attempts (BHCA), Centum Call Seconds (CCS), Erlangs, and average holding times (AHT). Essential for normalizing legacy telco CDR billing records to traffic intensity.

Inputs: Call Attempts (λ), Average Holding Time (h), or Direct CCS / Erlangs
Outputs: Erlangs (A), Hundred Call Seconds (CCS), Total Call Hours, Call Rate
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VoIP & SIP Sizing VoIP IP/UDP/RTP

SIP Trunk Concurrency & Bandwidth Planner

Dimension simultaneous SIP call sessions and aggregate WAN IP bandwidth based on voice codec selection (G.711 μ-law/a-law, G.729, Opus, G.722) including Layer 2/3 headers, RTP packetization, and VAD multiplexing.

Inputs: Simultaneous Calls, Audio Codec, Packet Sample Rate (20ms), VAD Status
Outputs: Per-Call Bandwidth (kbps), Aggregate WAN Throughput (Mbps), PPS
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Contact Center & Queuing M/M/1 & M/M/c

M/M/1 & M/M/c Kendall Notation Queuing Delay Analyzer

Evaluate single and multi-server Markovian queues, steady-state probability distributions, queue lengths (Lq), and system response wait times (W, Wq) using Little's Law and birth-death transition rates.

Inputs: Arrival Rate (λ), Service Rate (μ), Number of Servers (c)
Outputs: Utilization (ρ), Avg Queue Delay (Wq), Total Response Time (W), L & Lq
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Cellular & Radio Erlangs Radio GoS 0.02

Cellular Radio Erlang Capacity & Sector Blocking (GoS)

Dimension GSM, LTE VoLTE, and 5G NR VoNR radio channels per cell sector. Calculates trunking efficiency across multi-carrier transceivers (TRXs) and physical resource blocks under 2.0% cellular Grade of Service benchmarks.

Inputs: Radio Carriers / TRXs, Dedicated Signaling Slots, Target GoS (P.02)
Outputs: Supported Erlangs/Sector, Peak Subscriber Capacity, Trunking Gain
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Contact Center & Queuing WFM Staffing

Call Center Shrinkage, Occupancy & FTE Staffing Planner

Calculate raw agent requirements adjusted for planned and unplanned shrinkage (absenteeism, breaks, PTO, coaching) and maximum agent occupancy bounds to prevent workforce burnout while protecting SLA targets.

Inputs: Erlang C Raw Agents, Planned Shrinkage %, Unplanned Shrinkage %, Max Occupancy
Outputs: Required Scheduled FTEs, Effective Shrinkage Factor, On-Seat Headcount
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Foundations of Teletraffic Science & Stochastic Queuing Models

1. The Foundations of Teletraffic Science: Agner Krarup Erlang & Poisson Processes

In the early twentieth century, Danish mathematician and engineer Agner Krarup Erlang (1878–1929), working for the Copenhagen Telephone Company (KTAS), published foundational papers that formalized teletraffic engineering and modern applied queuing theory. Erlang addressed a profound practical challenge confronting early telecommunications networks: determining the exact number of copper circuits or switchboard operators required to handle incoming telephone calls with an acceptable probability of call completion, while preventing prohibitive capital over-expenditure on idle plant infrastructure.

Traffic Intensity and the Erlang Unit (E): Standardized by the CCITT (now ITU-T) in 1946, the Erlang is a dimensionless measure of telecommunications traffic intensity. One Erlang represents the continuous, uninterrupted occupation of a single transmission circuit over an observation interval of one hour (3,600 seconds of cumulative call duration). Mathematically, traffic intensity A is derived from the mean call arrival rate λ and the average call holding duration h:

ITU-T Recommendation E.500 Traffic Intensity Formulation
A = (λ × h) / T = λ × h (when T = 1 hour)

For example, an enterprise branch office experiencing 600 calls during its peak busy hour (λ = 600 calls/hr), with an Average Holding Time (AHT) of 180 seconds (3 minutes), generates:
A = (600 × 180) / 3600 = 108,000 / 3,600 = 30.0 Erlangs.
This means that at any random instant during the busy hour, an average of exactly 30 voice circuits are actively carrying conversation audio.

The Poisson Arrival Distribution: Erlang recognized that when telephone calls originate independently from a very large subscriber population, call arrivals follow a memoryless Poisson process. The probability P(k) that exactly k calls arrive within an arbitrary observation interval t is governed by:

Poisson Arrival Probability Distribution
P(k) = [ (λ × t)k × e-λt ] / k!

In parallel, call holding durations h are modeled as exponentially distributed random variables with service rate parameter μ = 1/h. The fundamental hallmark of the exponential distribution is its memoryless property: the probability that an active call continues for an additional Δt seconds is strictly independent of how long the call has already been in progress. In Kendall notation, this memoryless arrival and service behavior is denoted by the letter M (Markovian).

2. Lost-Calls-Cleared (LCC) vs. Lost-Calls-Delayed (LCD) Models

The fundamental bifurcation in classical teletraffic engineering divides operational systems into two distinct queuing philosophies:

Kendall's Standard Queuing Notation: Queuing systems are classified using the standardized six-parameter descriptor A / B / c / K / N / D:

The Retrial Phenomena — Extended Erlang B: In real-world telephony, human callers do not simply vanish upon hearing a fast-busy tone. A significant proportion of blocked subscribers (often 40% to 70%) immediately redial within seconds. The classical Erlang B model assumes zero retrials, which causes it to dangerously underestimate true blocking rates during network overload. Extended Erlang B (EEB) models subscriber recall probability R through an iterative convergence loop, mathematically inflating the raw offered load A into an expanded offered load A' until steady-state equilibrium is reached:

Extended Erlang B Recursive Traffic Inflation
A' = A + (A' × Pb(m, A') × R)  ⇒  A' = A / [ 1 - R × Pb(m, A') ]

3. The Engset Model: When Infinite Source Assumptions Fail

Both Erlang B and Erlang C rely on the assumption of an infinite subscriber population (N → ∞). This assumption holds true for municipal telecommunications switches serving hundreds of thousands of homes. However, when dimensioning private branch exchanges (PBX) in enterprise offices, shipboard satellite communication links, or industrial radio concentrators where the number of active telephone handsets S is comparable to the number of available external lines m (specifically when the source-to-server ratio S / m < 10), Erlang B yields excessively pessimistic blocking estimates.

In a finite source environment, every subscriber who originates a call and occupies an active trunk is temporarily removed from the pool of idle callers. As trunk utilization increases, the arrival rate of new calls automatically drops. The arrival process is strictly state-dependent: when k lines are busy, the instantaneous call arrival rate is λk = (S - k) × γ, where γ is the calling rate per idle subscriber.

The Engset Formula accounts for this negative feedback mechanism using binomial state distributions:

The Engset Loss Formulation (M/M/m/m/S)
Pcall = [ C(S - 1, m) × αm ] / [ ∑i=0m C(S - 1, i) × αi ]

Where C(n, k) = n! / [ k!(n - k)! ] is the binomial coefficient, and α = γ × h is the traffic generated per idle source.

Because idle callers cannot generate new calls while already engaged on a trunk line, the Engset model demonstrates that a finite user pool achieves a substantially lower blocking probability than predicted by Erlang B for identical nominal traffic levels.

4. VoIP & Modern Packet Voice Teletraffic Engineering

Modern telecommunications has largely transitioned from legacy time-division multiplexing (TDM) circuits (such as 64 kbps DS0 timeslots on T1/E1 PRIs) to packet-switched Voice over IP (VoIP) traversing SIP trunks and Carrier Ethernet backbones. While Erlang B remains the gold standard for determining the required number of simultaneous SIP sessions, network engineers must bridge teletraffic intensity to physical IP bandwidth (bits per second).

A single uncompressed VoIP call using the standard G.711 codec generates 64 kbps of raw pulse-code modulated (PCM) voice payload. However, transporting this audio over an IP packet network requires encapsulating the payload inside Real-time Transport Protocol (RTP), User Datagram Protocol (UDP), and Internet Protocol (IP) headers:

At the industry-standard packetization sample interval of 20 milliseconds (50 packets per second), each packet encapsulates:
Payload = 64,000 bps × 0.020 s = 1,280 bits = 160 Bytes.
Total Layer 3 packet size = 160 (Payload) + 12 (RTP) + 8 (UDP) + 20 (IP) = 200 Bytes.
Layer 3 Bandwidth = 200 Bytes × 8 bits/Byte × 50 packets/sec = 80.0 kbps per call.
With Layer 2 Ethernet framing (22 Bytes), the physical line rate is:
Physical Wire Rate = (200 + 22) × 8 × 50 = 88.8 kbps per concurrent call.

By contrast, a low-bitrate compressed codec like G.729 compresses speech to 8.0 kbps (20 Bytes payload per 20ms frame). When encapsulated with the same 40-byte IP/UDP/RTP overhead, the resulting Layer 3 packet size is 60 Bytes, consuming 24.0 kbps (or 32.8 kbps at Layer 2). Enabling Voice Activity Detection (VAD) and Comfort Noise Generation (CNG) leverages the statistical reality that human conversation involves one party listening while the other speaks, yielding approximately 35% statistical bandwidth savings across large multi-session SIP trunks.

Teletraffic Engineering Master Model Comparison Reference

The table below synthesizes the structural characteristics, Kendall notations, arrival assumptions, and typical carrier deployment contexts for all primary teletraffic formulations:

Teletraffic Model Kendall Notation Source & Arrival Assumptions Queuing & Buffer Discipline Primary Real-World Application
Erlang B M/M/m/m Infinite sources (∞), Poisson arrivals, exponential service Blocked Calls Cleared (Zero queue, instantaneous drop) PSTN voice trunks, SIP trunk sessions, E1/T1 PRI lines, PBX tie lines
Erlang C M/M/m/∞ Infinite sources (∞), Poisson arrivals, exponential service Blocked Calls Delayed (Infinite FIFO queue buffer) Call center agent staffing, customer support queues, IVR routing, help desks
Extended Erlang B M/M/m/m (Retrial) Infinite sources, Poisson base with retrial probability R Blocked Calls Retry Automatically (Traffic inflation loop) Congested toll routes, emergency hotlines, high-blocking voice links
Engset Model M/M/m/m/S Finite source pool (S < 10m), state-dependent arrivals Blocked Calls Cleared (Zero queue, idle source thinning) Small office key systems, hotel PBX pools, satellite channel concentrators
M/M/1 Queue M/M/1/∞ Poisson arrivals, single server, exponential service rate μ Infinite FIFO buffer, delay depends on server load ρ = λ/μ Single router output queues, serial data links, single-agent service windows
M/G/1 Queue M/G/1/∞ Poisson arrivals, general service distribution (mean & variance σ2) Pollaczek-Khinchine formula for mean waiting delay IP packet routers with variable packet length distributions (bimodal MTU)
M/D/1 Queue M/D/1/∞ Poisson arrivals, constant deterministic service time (zero variance) FIFO buffer; waiting time exactly half of M/M/1 queue at identical load ATM cell switches (fixed 53-byte cells), slotted TDM frame transmission